Unsupervised Adaptive Filtering: Blind source separation
A complete, one-stop reference on the state of the act of unsupervised adaptive filtering While unsupervised adaptive filtering has its roots in the 1960s, more recent advances in signal processing, information theory, imaging, and remote sensing have made this a hot area for research in several diverse fields. This book brings together cutting-edge information previously available only in disparate papers and articles, presenting a thorough and integrated treatment of the two major classes of algorithms used in the field, namely, blind signal separation and blind channel equalization algorithms. Divided into two volumes for ease of presentation, this important work shows how these algorithms, although developed independently, are closely related foundations of unsupervised adaptive filtering. Through contributions by the foremost experts on the subject, the book provides an up-to-date account of research findings, explains the underlying theory, and discusses potential applications in diverse fields. More than 100 illustrations as well as case studies, appendices, and references further enhance this excellent resource. Topics in Volume I include:
* Neural and information-theoretic approaches to blind signal separation
* Models, concepts, algorithms, and performance of blind source separation
* Blind separation of delayed and convolved sources
* Blind deconvolution of multipath mixtures
* Applications of blind source separation
Volume II: Blind Deconvolution continues coverage with blind channel equalization and its relationship to blind source separation.
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adaptive filtering algebra algorithm algorithms for blind Amari applications approach Bell and Sejnowski blind deconvolution blind equalization blind separation blind signal blind signal separation blind source separation Bussgang channel Cichocki coefficients contrast function convergence cost function covariance criterion decorrelation defined delays denotes density diagonal distribution divergence eigenvalues entropy estimation example extraction Figure FIR filter frequency domain Gaussian Haykin IEEE IEEE Trans independent component analysis Infomax input inverse iterations Jutten Kullback-Leibler divergence kurtosis learning algorithm learning rule linear maximization measure method minimization mixing matrix multichannel blind multipath mutual information natural gradient natural-gradient adaptation negentropy Neural Networks noise nonlinear number of sources on-line optimal orthogonal output signal parameter pdf's prewhitening problem Proc properties Renyi's samples Section sensors separating matrix Signal Processing signal separation source signals space statistically independent statistics system manifold tion transform unsupervised update variables whiteness constraint whitening wopt zero